Deep Tags: Toward a Quantitative Analysis of Online Pornography

by Antoine Mazieres, Mathieu Trachman, Jean-Philippe Cointet, Baptiste Coulmont and Christophe Prieur
The development of the web has increased the diversity of pornographic content, and at the same time the rise of online platforms has initiated a new trend of quantitative research that makes possible the analysis of data on an unprecedented scale. This paper explores the application of a quantitative approach to publicly available data collected from pornographic websites. Several analyses are applied to these digital traces with a focus on keywords describing videos and their underlying categorization systems. The analysis of a large network of tags shows that the accumulation of categories does not separate scripts from each other, but instead draws a multitude of significant paths between fuzzy categories. The datasets and tools we describe have been made publicly available for further study.